Analysis of the development of fruit trees diseases using modified analytical model of fuzzy c-means method

Ali Abdulkarem Habib Alrammahi, Farah Abbas Obaid Sari, Haidar Abdulwahab Habeeb Shamsuldeen


The use of digital technologies in agriculture has become very important to ensure the protection of trees from disease and limit their development, which leads to increased production, so the paper proposes a modified analytical model to analyze the data and graphical parts of the leaves of fruit trees using priority fuzzy C-means (PFCM). Based on the proposed distance scale to obtain a clustering with a less error rate and fairly close to accuracy for the purpose of monitoring the development of diseases of fruit trees, by classifying the diseases and medications needed for each disease, a database was created containing large samples of data and images, where the results of Analysis of previous studies that analyzes of large amounts of data give accurate results. The proposed method was used in smart gardens with large areas and we got the desired results.


Big data; Distance scale; False negative; False positive; Priority fuzzy C-means; Similarity coefficient; Smart garden

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